TL;DR: Experimental result shows that the improving algorithm can extract all moving objects, which was endowed with strong background adaptability and better real-time performance.
Abstract: This paper proposed methods of vehicle detection and tracking algorithm in real-time traffic. In the detection of realtime moving vehicle, vehicle areas would be determined through road line detection. Then, the main color information of moving and non-moving area would be obtained through frame difference. Filling the main color information in vehicle moving area would lead to a similar background image. At last, moving vehicles would be determined through adaptive Background Subtraction difference. In the tracking of moving vehicles, firstly, all characteristic corners can be got by using Harris detection. Then, all characteristic corner set in the separate moving area would be collected through cluster analysis. For each characteristic individual corner set can generate a circle embracing all characteristics, some problems like vehicle barrier could be analyzed by using the radius of characteristic circle. At last, conduct feature matching tracking by using the center of feature circle. Experimental result shows that the improving algorithm can extract all moving objects, which was endowed with strong background adaptability and better real-time performance. Keywords-target detection; frame difference method; background subtraction difference method; harris corner detection; clustering analysis
TL;DR: A detailed review of vehicle detection and classification techniques is presented and also discusses about different approaches detecting the vehicles in bad weather conditions and about the datasets used for evaluating the proposed techniques in various studies.
Abstract: Smart traffic and information systems require the collection of traffic data from respective sensors for regulation of traffic. In this regard, surveillance cameras have been installed in monitoring and control of traffic in the last few years. Several studies are carried out in video surveillance technologies using image processing techniques for traffic management. Video processing of a traffic data obtained through surveillance cameras is an instance of applications for advance cautioning or data extraction for real-time analysis of vehicles. This paper presents a detailed review of vehicle detection and classification techniques and also discusses about different approaches detecting the vehicles in bad weather conditions. It also discusses about the datasets used for evaluating the proposed techniques in various studies.
TL;DR: A novel image retrieval method based on improved K-means algorithm was presented, which took the two feature vectors which the distance between them is the maximum in the database, and found all correct initial centroids, and clustered according to the initial class Centroids.
Abstract: Having analyzed the drawbacks of image retrieval based on K-means algorithm,a novel image retrieval method based on improved K-means algorithm was presented in this paper.Firstly,computed the distance of every two color histograms of all color histograms in the image feature database.Then,took the two feature vectors which the distance between them is the maximum in the database,as the first two initial centroids,and found all correct initial centroids,and clustered according to the initial class centroids.Finally,started image retrieval.Experimental results demonstrate that the proposed method is efficient.
TL;DR: A vision-based system that, coupling them in a rule-based fashion, is able to detect and track vehicles and allows the generation of an interface that informs a driver of the relative distance and velocity of other vehicles in real time and triggers a warning when a potentially dangerous situation arises.
Abstract: Detecting car taillights at night is a task which can nowadays be accomplished very fast on cheap hardware. We rely on such detections to build a vision-based system that, coupling them in a rule-based fashion, is able to detect and track vehicles. This allows the generation of an interface that informs a driver of the relative distance and velocity of other vehicles in real time and triggers a warning when a potentially dangerous situation arises. We demonstrate the system using sequences shot using a camera mounted behind a car’s windshield.
55 citations
"Research on Vehicle Detection and T..." refers methods in this paper
...The key techniques of that includes vehicle detection, image pre-process, vehicle tracking, identification and etc [1-2]....
TL;DR: In this article, the L-shaped corner points were selectively recognized from all kinds of corner points in the detecting image by improving the traditional Harris corner detection algorithm, and the position accuracy of corners was promoted by sub-pixel post-processing.
Abstract: To recognize and detect a rectangle rapidly and accurately,an image collection system was established and a rapid detection algorithm for rectangles was proposed based on Harris corner detection algorithm.First,the L-shaped corner points were selectively recognized from all kinds of corner points in the detecting image by improving the traditional Harris corner detection algorithm,and the position accuracy of corner points was promoted by sub-pixel post-processing.Then,according to the obtained high-precision angular position information,some parallel straight line segment pairs with equal length were grouped and the perpendicular parallel line segment pairs with four overlap corner points were matched,by which the four sides of a rectangle were obtained.Furthermore,all the rectangle elements were detected in the processed image.In order to improve the accuracy and reliability of the rectangular recognition algorithm,the identifying criterion of the pseudo rectangular graphic elements was provided.Finally,the sensor performance testing experiments were carried out.Experimental results indicate that the rectangular recognition speed of Harris corner point algorithm is 8.5times faster than that of Hough algorithm,and the rectangular image recognition maximum position error is 0.4pixel.The Harris corner rectangle detection method has strong anti-interference capability and stability and can meet thehigh real-time and precision detection requirements of industrial application.
11 citations
"Research on Vehicle Detection and T..." refers methods in this paper
...After Harris corner detection[14], the center of gravity of the vehicle can be obtained through analyzing detected corner set....
TL;DR: The algorithm is simple and effective, with a small amount of calculation, which can locate the information of high-definition video vehicles more accurately, and achieve vehicle location and tracking, and meet the real-time processing requirement.
Abstract: The technology of high-definition video detection has the virtue of high resolution, which can clearly detect vehicle information and license plates, etc. In this paper, according to the characteristics of high-definition video detection technology, we propose a new vehicle location and tracking method, which is based on the brightness curve. Firstly, break up the image by regions, and then we can get the brightness curve of each lane by doing horizontal projection. Then do the background modeling to the brightness curve, thus the horizontal vehicle division is completed. Do adaptive edge detection to the regions divided by lanes, and then we can get the vertical location of vehicles by doing the vertical projection and adaptive filtering. After the completion of vehicle location, do prediction and tracking of vehicles using the algorithm presented in this paper which is based on brightness curve. Experiments show that the algorithm is simple and effective, with a small amount of calculation, which can locate the information of high-definition video vehicles more accurately, and achieve vehicle location and tracking. This algorithm can basically meet the real-time processing requirement.
6 citations
"Research on Vehicle Detection and T..." refers methods in this paper
...Interframe difference method [7-8] applies the difference of two or three adjacent images to obtain the moving target area, which is of strong adaptability and robustness to the moving target....
TL;DR: Experiments shows that proposed discrete wavelet transform based method has a high capability to detect and track non-rigid moving object, even when light intensities change abruptly.
Abstract: A robust, meticulous and high performance approach is still a great challenge in tracking approach. There are various difficulties in object tracking like noise in scene, illumination changes, occlusion effect, and pose variation into the scene. As an object moves, it changes its orientation relative to the light sources which illuminate it. An illumination variation causes tracking algorithm to lose the target in the scene. This paper presents a discrete wavelet transform based method of detecting and tracking moving object under varying illumination condition with a stationary camera. Discrete wavelet transform provides illumination invariant feature extraction method using gaussian smoothing function and thresholding. We have tested tracking results, on number of video sequences with indoor and outdoor environments and demonstrated the effectiveness of our proposed method. Experiments shows that proposed method has a high capability to detect and track non-rigid moving object, even when light intensities change abruptly.
3 citations
"Research on Vehicle Detection and T..." refers methods in this paper
...At present, the major approaches to detect moving vehicles are inter-frame difference method, Background Subtraction difference method, optical flow method and etc [3-4]....
[...]
...Background Subtraction difference method[3-4] requires first setting up the Gaussian background model as the background image, then calculating the difference of pixel brightness between current video sequence image and the known background image and taking the absolute value....
TL;DR: The experimental results confirm that the motive target can be detected effectively through the analysis of the IR detector work states and the intelligent transform of motion detection algorithms.
Abstract: In order to detect the motive target in the complex infrared image sequences obtained by IR detector under different work states,a motion detection combination algorithm was proposed using image subtraction and Lucas Kanade optical flow method.According to the work states of IR detector,the relative motion relationship was established between the IR detector and the target.Image difference method and Lucas Kanada optical flow method were adopted respectively to detect the target motion,when the IR detector was still or motive.The emulation and analysis were carried out for the infrared image sequences obtained by IR detectors.The experimental results confirm that the motive target can be detected effectively through the analysis of the IR detector work states and the intelligent transform of motion detection algorithms.
3 citations
"Research on Vehicle Detection and T..." refers methods in this paper
...The optical flow method [9-10] is to detect the characteristics of every pixel in each frame of image sequence....